Most AI never makes it past the demo. We get yours into production.

# Ikokas — Most AI never makes it past the demo. We get yours into production. > Ikokas — AI services, from ambition to production. The hard part is rarely the model. It is your data, the controls, and the workflow change. We build private generative AI grounded in your own knowledge, agents that act with a person in the loop, and the governance that gets them live. - [Talk to Us](/contact) - [Get an Audit](/audit) ## We're reading - **Models** — Frontier labs ship longer-context enterprise models with tighter data controls. - **Regulation** — EU AI Act August obligations move from guidance to enforcement. - **Adoption** — Survey finds retrieval over private data now the most-deployed enterprise pattern. - **Agents** — Analysts warn over 40% of agentic pilots risk cancellation without controls. - **Research** — New work on grounding and citation faithfulness in retrieval systems. - **Security** — Guidance tightens on model supply-chain and prompt-injection defence. - **Product** — Vector databases add native access-control and audit primitives. - **Search** — Answer engines keep taking share from traditional search for buyer research. ## Built for the people accountable, and the machines that read. **Human in the loop.** A person stays accountable for every consequential decision an agent or model makes. We design where people review, approve, and override before a workflow goes live, so automation earns trust rather than asking for it. It is also why fewer of our agentic projects stall, against the over 40% the industry expects to cancel on cost, unclear value, and weak controls. [3] **Built for machines, not only people.** The customers, staff, and buyers who matter to your business increasingly ask an AI assistant before they ask you, and knowledge now gets retrieved before it gets read. Gartner expects a quarter of traditional search to move to AI chatbots and agents by 2026. So we build your systems, and structure the knowledge inside them, to be retrieved and quoted accurately by an answer engine, not read by a person alone. [8] ## Everything it takes to reach production. Getting AI into production takes far more than a model. It takes the audit that decides whether to build, the private generative AI and agents that do the work, and the security, design, and engineering that put them live. ### 01 · AI Strategy and Success Audit Know if your AI bet will pay off before you build it, because most initiatives stall after the demo. [2] - Success Probability Audit — An honest read on whether your initiative will land, and what would have to be true for it to. - Legacy Application Transformation — Modernise ageing applications with AI without betting the business on a full rebuild. - Technical Feasibility Scorecard — Know what is buildable, and at what risk, before you commit budget. - ROI Projection Modeling — A defensible number for the business case, built on your own inputs. - 90-Day Implementation Roadmap — A sequenced plan to first real value inside one quarter. ### 02 · Generative AI and Private RAG Answers from your own knowledge, kept private and grounded in your sources. [6] - Private RAG Implementation — Your documents answered accurately and with citations, without your data leaving your control. - Vector Database Setup — Retrieval infrastructure tuned to your corpus, so the right context reaches the model. - Semantic Search Interface — Search that understands meaning rather than matching keywords. - SOC2-Ready Data Architecture — Built for the access controls and evidence your auditors will ask for. ### 03 · Autonomous AI Agents Software that does the work, with a person in the loop on what matters. [3] - Agentic Workflow Automation — Hand repetitive, rule-bound work to agents that run reliably and log what they did. - Agent Logic Mapping — Every decision path designed and reviewed before an agent ships. - CRM and ERP Integration — Agents that act inside the systems you already run, not beside them. - Human-in-the-loop Dashboards — People approve, override, and audit the steps that carry real consequence. ### 04 · AI Security and Governance Ship AI that passes legal, audit, and the regulator. [4][7] - AI Security and Governance Audit — Find the risk in a model or pipeline before the regulator or an incident does. - GDPR and EU AI Act Compliance — Meet the obligations that now apply to AI systems in regulated markets. - Bias and Risk Mitigation — Models tested and documented so they hold up to real scrutiny. - Compliance Certification Prep — Audit-ready evidence, organised the way assessors expect to receive it. ### 05 · Data-Native UI/UX Design Interfaces designed on behaviour and built to convert. - Behavioral Science Audit — Why users drop off, backed by evidence rather than opinion. - Heatmap and Conversion Analysis — See exactly where attention and revenue leak out of the product. - Interactive Prototyping — Test the experience with real users before engineering builds it. - High-Fidelity UI Design — Production-ready design your engineers can ship without guesswork. ### 06 · AI Expert-on-Demand Senior AI engineers embedded in your team for as long as you need them. - Embedded AI Engineering — Senior talent working as part of your team, shipping alongside your people. - AI Architect on Retainer — A principal architect on call for the decisions that are expensive to get wrong. - AI Code Reviews — A second set of expert eyes on every release, before it reaches production. - Knowledge Transfer — Your team owns and can extend the system after we step back. ### 07 · Product Development From idea to launched product, with one accountable team. - Ideation and Discovery — Find the product worth building before a line of code is written. - UI/UX Design — Designed to convert from the first release. - Development — Shipped fast and built to last, on a stack your team can maintain. - Digital Marketing — Launched to the right audience, measured against real adoption. ## What are you trying to ship? Most teams come to us with a job to be done, not a list of capabilities. Pick the one that sounds like your quarter, and we will show you the shortest honest path to it. - **Answers from our own knowledge, with sources** — A private assistant that answers from your documents, and shows where each answer came from. First step: start with a Private RAG pilot on one high-value document set. Maps to: Generative AI and Private RAG. - **Sense out of scattered documents** — Turn contracts, reports, and records into answers a team can act on. First step: index one messy corpus and prove retrieval quality first. Maps to: Generative AI and Private RAG. - **Rule-bound work moved to agents** — Hand repetitive work to agents, with a person approving what matters. First step: map one workflow and place the human-approval step. Maps to: Autonomous AI Agents. - **AI that clears our regulator** — Ship AI built to meet GDPR and the EU AI Act. First step: run a governance audit against your obligations. Maps to: AI Security and Governance. - **A product that converts** — Interfaces designed on real behaviour and built to convert. First step: audit the drop-off, then prototype the fix. Maps to: Data-Native UI/UX Design. - **Senior AI engineers inside our team** — Embedded talent that ships alongside your people. First step: embed a senior engineer for one sprint. Maps to: AI Expert-on-Demand. - **A product taken from idea to launch** — From first idea to launched product, with one accountable team. First step: scope discovery and a first shippable release. Maps to: Product Development. ## How we work The reason most AI stalls is the same. Teams build a model that demos well, then find the value was always in the workflow, the data, and the controls around it, where redesigning the workflow moves the bottom line more than the technology and only about 1 in 5 organisations have done it. [1][4] We work the other way around: a demo, resolved into a system that ships. 1. **Demo** — We pressure-test the idea before you spend, so budget goes to what will actually reach production. 2. **Pressure-test** — A person stays accountable for every consequential step, and the system is built to pass audit from the first release. 3. **Build** — Governance and evidence are part of the build, mapped to obligations such as the EU AI Act, not bolted on at the end. [7] 4. **In production** — We build on standard platforms and hand over the architecture and the decisions, so your team can run and extend it without us. ## Where the stakes are highest We work where data is sensitive and every decision is scrutinised, because that is where private, reliable, defensible AI stops being optional. What changes from one sector to the next is the rules you operate under, the evidence you produce, and the data you can move. - **Financial Services** — Every decision has to be evidenced, so every model output traces to a source and a person. - **Healthcare** — Patient data cannot leak to public models or unauthorized staff. - **Legal** — Citations must be exact. Hallucinations in case law destroy credibility. - **SaaS** — AI features must scale to millions of requests without degrading performance. - **Retail** — Agents must connect inventory, support, and sales across siloed systems. - **Manufacturing** — Predictive maintenance models must run at the edge, near the equipment. ## In their words > "Ikokas got our retrieval assistant past our own risk committee on the first pass. The audit trail did the convincing, not the demo." — Head of Data Platform, European fintech > "We had three stalled AI pilots. They killed one, fixed one, and shipped the third inside a quarter." — Director of Engineering, healthcare software > "The agents run in production, and we can see and override every step that carries real consequence." — VP Operations, logistics group ## Latest insights Practical thinking on private AI, retrieval, agents, and AI governance for teams putting AI into production. - [What the EU AI Act's August obligations actually ask of your models](/resources/insights/eu-ai-act-august-obligations/) — 2026.06.24 · Governance - [Why retrieval beats fine-tuning when your knowledge keeps changing](/resources/insights/retrieval-over-fine-tuning/) — 2026.06.10 · Retrieval - [Designing the approval step so agents ship instead of stall](/resources/insights/designing-the-approval-step/) — 2026.05.28 · Agents - [What a risk committee actually asks before it approves an AI system](/resources/insights/what-a-risk-committee-asks/) — 2026.05.15 · Governance - [The demo trap, and how to design past it](/resources/insights/the-demo-trap/) — 2026.05.02 · Product - [Citations are the feature, not the footnote](/resources/insights/citations-are-the-feature/) — 2026.04.20 · Retrieval ## How an engagement starts Most relationships begin with a Success Probability Audit. Over a couple of weeks we pressure-test the idea against your data, your systems, and the rules you operate under, then return an honest read on whether it is worth building and what it would take. The audit is the smallest first step that proves value, so you can judge both the opportunity and the way we work before committing to anything larger. - [Get an Audit](/audit) - [Talk to Us](/contact) ## Questions, answered **What does Ikokas actually do?** We take AI from idea to production for companies that need it to be private, reliable, and defensible. The work spans the strategy audit that decides whether to build, private generative AI and retrieval over your own knowledge, autonomous agents with a person in the loop, AI security and governance, data-native design, embedded engineering, and full product development. **Will this pay off, or will it stall after the demo like most AI projects?** That is the first thing the audit answers. At least 30% of gen AI projects are abandoned after the proof of concept, usually on weak data, unclear value, or missing controls, so we pressure-test those before you commit and design the workflow change that actually creates the value. [1][2] **Why retrieval over our own data rather than fine-tuning a model on it?** Retrieval grounds answers in your current documents and returns citations, so the system stays accurate as your knowledge changes and you can see where each answer came from, without exposing your data in a model's weights. [6] **Is our data ready for AI?** Often it is not, which is why 63% of organizations lack the data management practices AI needs and many projects stall on the foundation. We make the data and retrieval AI-ready first, so a model or agent has something dependable to run on. [5] **Can agents do real work without us losing control?** Yes, that is the point of human-in-the-loop design. A person approves, overrides, and audits every consequential step, so control is designed in from the start rather than traded away for automation. That is what separates the agentic projects that reach production from the many that Gartner expects to be cancelled after pilot. [3] **Can you help us meet AI regulation?** We build AI security and governance into the system and prepare the evidence assessors expect, mapped to obligations such as GDPR and the EU AI Act, whose enforcement powers now apply. We do not provide legal advice, and your legal and compliance teams own the specific obligations, but the system is built to make meeting them practical rather than a scramble. [7] **Will our systems and our own knowledge be found and quoted by the AI assistants people now use?** Increasingly that is how customers, staff, and buyers find answers. Gartner expects a quarter of traditional search to move to AI chatbots and agents by 2026, so we build your systems and structure your knowledge to be retrieved and quoted accurately by answer engines, not only ranked in search. [8] **Will we be locked into Ikokas?** No. We build on standard platforms and open components, document the architecture and the decisions, and transfer ownership to your team, so you can run and extend the system yourselves or move to another partner. **How does a relationship usually start?** Most start with a Success Probability Audit. We pressure-test the idea against your data, systems, and rules, and return an honest read on whether to build, what it would take, and what it is worth, so the first step is small and the path is clear. ## Find out if your AI initiative will pay off, before you build it. Tell us what you are trying to do. A short audit will show you whether to build, what it would take, and what it is worth. - [Get an Audit](/audit) - [Talk to Us](/contact) ## References 1. McKinsey & Company (QuantumBlack). "The state of AI in 2025." November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai 2. Gartner. "30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025." 29 July 2024. https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025 3. Gartner. "Over 40% of Agentic AI Projects Will Be Canceled by End of 2027." 25 June 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027 4. Deloitte. "State of Generative AI in the Enterprise, Quarter four 2024." https://www.deloitte.com/us/en/about/press-room/state-of-generative-ai.html 5. Gartner. "Lack of AI-Ready Data Puts AI Projects at Risk." 26 February 2025. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk 6. Lewis, Perez, Piktus, et al. "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020. https://arxiv.org/abs/2005.11401 7. Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union, 2024. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng 8. Gartner. "Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots." 19 February 2024. https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents